Omar I. Al Helalat

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In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature(More)
In this paper, an Arabic character recognition system based on Artificial Neural Networks (ANN) and statistical analysis of the Arabic characters is presented. In this system, each typed Arabic character is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to an ANN that consists of two layers.(More)
In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each(More)
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